From the uDemy course on LLM engineering.
https://www.udemy.com/course/llm-engineering-master-ai-and-large-language-models
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194 lines
4.5 KiB
194 lines
4.5 KiB
{ |
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"cells": [ |
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{ |
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"cell_type": "markdown", |
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"id": "b4fcc94e-6e57-450e-8de7-b757834b6d9f", |
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"metadata": {}, |
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"source": [ |
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"### Here's a class with an `__init__` thingy and a method" |
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] |
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}, |
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{ |
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"cell_type": "code", |
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"execution_count": 3, |
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"id": "f2c48975-7736-4f72-9e47-7c9df5b534df", |
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"metadata": { |
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"editable": true, |
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"slide_type": "" |
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"tags": [] |
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}, |
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"outputs": [ |
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{ |
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"name": "stdout", |
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"output_type": "stream", |
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"text": [ |
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"This is an oldtimer.\n" |
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] |
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} |
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], |
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"source": [ |
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"class Car:\n", |
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" def __init__(self, brand, year):\n", |
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" self.brand = brand\n", |
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" self.year = year\n", |
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"\n", |
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" currentYear = 2025\n", |
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"\n", |
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" def isOldTimer(self):\n", |
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" age = 2025 - self.year\n", |
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" if(age > 30):\n", |
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" print(\"This is an oldtimer.\")\n", |
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" else:\n", |
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" print(\"This isn't an oldtimer yet.\")\n", |
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"\n", |
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"myCar = Car(\"Bentley\", 1967)\n", |
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"\n", |
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"myCar.isOldTimer()" |
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] |
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}, |
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{ |
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"cell_type": "markdown", |
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"id": "7dc40be0-a3af-49cf-93e4-14134c75325a", |
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"metadata": { |
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"editable": true, |
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"slideshow": { |
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"slide_type": "" |
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}, |
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"tags": [] |
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}, |
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"source": [ |
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"### Here's what I learned today about `yield`, _comprehension_, and _sets_" |
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] |
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}, |
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{ |
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"cell_type": "code", |
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"execution_count": 4, |
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"id": "f726fc34-8b87-482a-9100-05d26e2853db", |
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"metadata": {}, |
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"outputs": [], |
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"source": [ |
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"bentley = {\"brand\": \"Bentley\", \"category\": \"sporty luxury vehicles\"}\n", |
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"volkswagen = {\"brand\": \"Volkswagen\", \"category\": \"lackluster utilitarian vehicles\"}\n", |
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"jaguar = {\"brand\": \"Jaguar\", \"category\": \"sporty luxury vehicles\"}\n", |
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"koenig = {\"brand\": \"Koenigsegg\"}\n", |
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"default = {\"category\": \"default vehicle\"}\n", |
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"\n", |
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"cars = [bentley, volkswagen, jaguar, koenig]\n", |
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"\n", |
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"#A 'comprehension' is a shorthand for defining lists, sets, dictionaries, and tuples\n", |
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"brands = [car.get(\"brand\") for car in cars if car.get(\"brand\")]\n", |
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"\n", |
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"#Here's a comprehension for a set. A set is like a list, but unordered, and it can only have unique values\n", |
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"categories = {car.get(\"category\") for car in cars if car.get(\"category\")}" |
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] |
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}, |
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{ |
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"cell_type": "code", |
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"execution_count": 5, |
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"id": "c94a579a-7229-4d19-b445-d70b20dbc731", |
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"metadata": {}, |
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"outputs": [ |
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{ |
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"name": "stdout", |
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"output_type": "stream", |
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"text": [ |
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"['Bentley', 'Volkswagen', 'Jaguar', 'Koenigsegg']\n" |
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] |
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} |
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], |
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"source": [ |
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"print(brands)" |
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] |
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}, |
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{ |
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"cell_type": "code", |
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"execution_count": 6, |
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"id": "1d99825f-4e1c-4846-bc44-3001ea85df75", |
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"metadata": {}, |
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"outputs": [ |
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{ |
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"name": "stdout", |
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"output_type": "stream", |
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"text": [ |
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"{'lackluster utilitarian vehicles', 'sporty luxury vehicles'}\n" |
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] |
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} |
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], |
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"source": [ |
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"print(categories)" |
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] |
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}, |
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{ |
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"cell_type": "code", |
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"execution_count": 10, |
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"id": "59fd4d0b-c9de-44a9-8205-8b8353940481", |
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"metadata": { |
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"editable": true, |
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"slideshow": { |
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"slide_type": "" |
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}, |
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"tags": [] |
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}, |
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"outputs": [ |
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{ |
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"name": "stdout", |
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"output_type": "stream", |
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"text": [ |
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"Bentley\n", |
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"Jaguar\n", |
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"Koenigsegg\n" |
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] |
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} |
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], |
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"source": [ |
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"#'yield' is comparable to 'return' with the difference that it doesn't load entire lists to memory\n", |
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"#btw, 'from' allows for a more condensed way of a 'for x in y' statement\n", |
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"import time\n", |
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"\n", |
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"def listBrands():\n", |
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" yield from [brand for brand in brands if not brand.startswith('V')]\n", |
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"\n", |
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"for brand in listBrands():\n", |
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" print(brand)\n", |
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" time.sleep(1)" |
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] |
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}, |
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{ |
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"cell_type": "code", |
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"execution_count": null, |
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"id": "6c52158e-1786-4638-a7c7-add61d932459", |
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"metadata": { |
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"editable": true, |
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"slideshow": { |
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"slide_type": "" |
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}, |
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"tags": [] |
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}, |
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"outputs": [], |
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"source": [] |
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} |
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], |
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"metadata": { |
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"kernelspec": { |
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"display_name": "Python 3 (ipykernel)", |
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"language": "python", |
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"name": "python3" |
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}, |
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"language_info": { |
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"codemirror_mode": { |
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}, |
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"file_extension": ".py", |
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"mimetype": "text/x-python", |
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"name": "python", |
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"nbconvert_exporter": "python", |
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"pygments_lexer": "ipython3", |
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"version": "3.11.11" |
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} |
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"nbformat": 4, |
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"nbformat_minor": 5 |
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